Efficient Approximate Membership Localization using P-Prune algorithm in blogs

Approximate Membership Localization (AML) is concerned with locating non-overlapped substrings thus avoiding redundancies. This overcomes the drawback of Approximate Membership Extraction (AME) process which has low efficiency for real world application. An algorithm called P-Prune is used in Blog search. This prunes most of the overlapped redundant substrings before generating them. Here we use the opinion retrieval scheme which analyses the viewers´ comments on Blog contents. Our experimental study on blogs reveals the efficiency of P-Prune over AME method. We also work AML in application to a proposed Blog search framework, a search-based approach joining two tables using dictionary-based entity recognition from blogs. Apart from the advantage of AML over AME, the experiment also proves the efficiency of the search-based approach. This algorithm can be extended to video blogs (vlog) also.